Empowering high-speed train positioning: Innovative paradigm for generating universal virtual positioning big data

High-speed trains (HSTs) positioning is a critical technology that affects the safety and operational efficiency of trains. The unique operating environment of HSTs, coupled with the limitations of real data collection, poses challenges in obtaining large-scale and diverse positioning data. To tackle this problem, we introduce a comprehensive method for generating virtual position data for HSTs. Utilizing virtual simulation technology and expert expertise, this method constructs a HST operating simulation environment on the Unity 3D platform, effectively simulating a range of operating scenarios and complex scenes. Positioning data is collected using virtual sensors, while error characteristics are incorporated to emulate real data collection behavior. The contribution of this paper lies in providing abundant, reliable, controllable and diverse positioning data for HSTs, thereby offering novel insights and data support for the evaluation and optimization of positioning algorithms. This method is not only applicable to various routes and scenarios, but also delivers fresh perspectives on data generation for research in other domains, boasting a broad scope of application.